https://github.com/modelscope/modelscope
공식 코드VideoFusion: Decomposed Diffusion Models for High-Quality Video Generation
Papers with Code 아카이브
Emotion Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 12
- 평가표
- 10
- 보존된 평가 기록
- 18
Emotion Recognition is an important area of research to enable effective human-computer interaction. Human emotions can be detected using speech signal, facial expressions, body language, and electroencephalography (EEG). Source: Using Deep Autoencoders for Facial Expression Recognition
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/monaen/LightFieldReconstruction
공식 코드High-dimensional Dense Residual Convolutional Neural Network for Light Field Reconstruction
https://github.com/mostly-ai/paper-fidelity-accuracy
공식 코드Holdout-Based Fidelity and Privacy Assessment of Mixed-Type Synthetic Data
https://github.com/mrT23/TResNet
공식 코드https://github.com/mrkoujan/FER
공식 코드Real-time Facial Expression Recognition "In The Wild'' by Disentangling 3D Expression from Identity
https://github.com/mrluin/ESFNet-Pytorch
공식 코드ESFNet: Efficient Network for Building Extraction from High-Resolution Aerial Images
https://github.com/mrrezaeiuoft/deep_direct_discriminative_decoder-d4-
공식 코드Deep Direct Discriminative Decoders for High-dimensional Time-series Data Analysis
https://github.com/mrzarei5/vax-culture
공식 코드Vax-Culture: A Dataset for Studying Vaccine Discourse on Twitter
https://github.com/msjae/abaw
공식 코드https://github.com/mspranger/icdl2016language
공식 코드Referential Uncertainty and Word Learning in High-dimensional, Continuous Meaning Spaces
https://github.com/mtran14/dp_w2v2
공식 코드A Speech Representation Anonymization Framework via Selective Noise Perturbation
https://github.com/multimedia-berkeley/OriSet
공식 코드OrigamiSet1.0: Two New Datasets for Origami Classification and Difficulty Estimation
https://github.com/mustafaCoskunAgu/ScNP
공식 코드Graph Convolutional Networks Meet with High Dimensionality Reduction
https://github.com/muzixiang/si-cleer
공식 코드https://github.com/mvanness354/missing_indicator_method
공식 코드https://github.com/mvasil/fashion-compatibility
공식 코드https://github.com/myHaiven/data-collection
공식 코드BERSting at the Screams: A Benchmark for Distanced, Emotional and Shouted Speech Recognition
https://github.com/mygithub-ax/8th_abaw
공식 코드Feature-Based Dual Visual Feature Extraction Model for Compound Multimodal Emotion Recognition
https://github.com/naamahadad/A-Two-Step-Disentanglement-Method
공식 코드https://github.com/naivete5656/WSISPDR
공식 코드Weakly Supervised Cell Instance Segmentation by Propagating from Detection Response
https://github.com/natkowalczyk/thermal-mask-classification-and-detection
공식 코드https://github.com/natspeech/natspeech
공식 코드PortaSpeech: Portable and High-Quality Generative Text-to-Speech
https://github.com/neclow/serab
공식 코드SERAB: A multi-lingual benchmark for speech emotion recognition
https://github.com/neuralchen/CooGAN
공식 코드CooGAN: A Memory-Efficient Framework for High-Resolution Facial Attribute Editing
https://github.com/neuralchen/SimSwap
공식 코드SimSwap: An Efficient Framework For High Fidelity Face Swapping
https://github.com/neuroailab/Neural-Alignment
공식 코드Two Routes to Scalable Credit Assignment without Weight Symmetry
https://github.com/nguyenthaivu/sadda
공식 코드Semi-Supervised Adversarial Discriminative Domain Adaptation
https://github.com/nicaogr/multiresolution_texture
공식 코드High resolution neural texture synthesis with long range constraints
https://github.com/nickdeas/masive
공식 코드MASIVE: Open-Ended Affective State Identification in English and Spanish
https://github.com/nickyfot/emocommonsense
공식 코드VLLMs Provide Better Context for Emotion Understanding Through Common Sense Reasoning
https://github.com/nicob15/deepkernellearningofdynamicalmodels
공식 코드Deep Kernel Learning of Dynamical Models from High-Dimensional Noisy Data
https://github.com/nicolas-richet/feature-vs-text-compound-emotion
공식 코드Textualized and Feature-based Models for Compound Multimodal Emotion Recognition in the Wild
https://github.com/nifangbaage/coordfill
공식 코드CoordFill: Efficient High-Resolution Image Inpainting via Parameterized Coordinate Querying
https://github.com/nilesc/Long-Structured-Debate-Generation-and-Evaluation
공식 코드https://github.com/ninglab/g2retro
공식 코드$\mathsf{G^2Retro}$ as a Two-Step Graph Generative Models for Retrosynthesis Prediction
https://github.com/nkegke/deep-affective-bodily-expression-recognition
공식 코드Medical Face Masks and Emotion Recognition from the Body: Insights from a Deep Learning Perspective
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.